1. Technical Field
An embodiment of the present invention generally relates to a distributed speech recognition system. More particularly, an embodiment of the present invention relates to a distributed speech recognition system that creates a statistical model of a noise vector.
2. Discussion of the Related Art
Although distributed speech recognition (“DSR”) is not a new concept, it has only recently been formalized through the European Telecommunications Standardization Institute (“ETSI”) Aurora standard, ETSI ES 201 108 V1.1.2 (2000–04), published April 2000. Thus, few (if any) commercial DSR systems currently exist.
DSR systems that have mobile clients with embedded microphones, as opposed to head-worn microphones, encounter significant acoustic background noise. Parallel model combination (“PMC”) is an attractive approach to combat such noise; however, to be effective, PMC requires a good estimate of the background noise. An example of a PMC method is specified in M. F. J. Gales and S. J. Young, “A Fast and Flexible Implementation of Parallel Model Combination,” Proc. International Conference on Acoustics Speech and Signal Processing (“ICASSP”) '95, May 1995, pp. 133–136.
DSR systems using PMC require a sufficient number of noise feature vectors in order to accurately model noise and to accurately adjust acoustic models. A feature signal waveform. In other words, the feature vector may be described as a parametric representation of the given time-segment of the signal waveform. Noise feature vectors are typically separated in time from speech feature vectors by applying a voice activity detector. The number of noise feature vectors required for PMC, for example, may have a significant impact on a DSR client's battery life, particularly in time-varying acoustic environments where frequent noise model updates are necessary. Providing a higher number of noise feature vectors consumes more transmission bandwidth and may require a system's radio transmitter to run more frequently and/or for longer duration, thereby draining the system's battery more quickly. Similarly, if the system continuously runs an analog-to-digital (“A/D”) converter to measure the noise floor, the battery life will be reduced.